Summary: The purpose of this study is to assess the repellent activity of lemongrass (Cymbopogon citratus) essential oil (EO), grown in Serbia under greenhouse conditions, against four prevalent stored product pests: Plodia interpunctella (larvae), Sitophilus oryzae, Acanthoscelides obtectus and Tribolium castaneum (adults). The lemongrass EO repellency was tested using filter paper in Petri dishes and a Y-tube olfactometer. According to the repellency index (RI), the lemongrass EO repellency was divided into 5 classes. Prior to biotesting, the chemical characterization of lemongrass EO was performed and the following main compounds were detected: myrcene (31.0%), geranial (30.0%), and neral (23.6%). The C. citratus EO considered was found to exhibit the Class III repellent activity against P. interpunctella larvae only at the highest concentration (namely 0.5%). This is the very first report on the C. citratus EO repellent activity against this pest. The lemongrass EO examined showed strong repellency (Class IV) against S. oryzae (0.2% and 0.5% of EO), A. obtectus (0.1% and 0.2%), and T. castaneum (0.05-0.1%). Moreover, higher lemongrass EO concentrations (0.5%) were found to exhibit extreme repellency (Class V) against A. obtectus and T. castaneum. The results obtained were confirmed in the bioassays performed, indicating the great potential of lemongrass EO as a bio-repellent when applied in higher concentrations to all the insects considered, regardless of the exposure period.

The biological activity (contact and contact-digestive toxicity, repellent and fumigant effects, effect on the insect’s development and life cycle parameters) of industrial hemp (Cannabis sativa L.) ethanolic extract was assessed against Plodia interpunctella, the most destructive storage pest of sunflower. Additionally, the study aimed to examine the phytotoxic activity of the extract in order to assess its potential as a sunflower grain protectant. Phytotoxicity assessment was based on the effect on germination energy and seed germination and the activity of antioxidative enzymes, enzymes of the polyphenolic metabolism, and the intensity of lipid peroxidation in sunflower seedlings. The antioxidant capacity and content of phenolic compounds (total phenolics and total tannins) were also measured in seedlings. In the experiments, 70% ethanolic extract of dried flowering buds of industrial hemp (variety Helena) was applied at 0.5%, 1.0% and 2.0% concentrations directly on sunflower seeds. Ethanol solution (70%) was the control. The hemp extract (1% and 2%) revealed medium repellence for P. interpunctella larvae (L_3–4) while at 2% concentration it caused 42.5% larval mortality after 72 h. Moreover, the insect’s development was prolonged and fecundity significantly reduced in hemp treatments. The extract did not exhibit fumigant activity. Germination energy and germination of sunflower seeds were stimulated in treatment with 2% hemp extract, while most biochemical parameters of the seedlings were not significantly affected by the hemp extract.

Climate change projections for the 21st century pose great threats to semi-arid regions, impacting seed production and the quality of sunflowers. Crop yields are negatively affected by climate variability, especially in the event of droughts during the crucial growth stages. Understanding the relationships between agrometeorological, genetic, and agronomic factors is crucial for maintaining crop sustainability. Optimal sowing dates are an essential condition for maximizing crop genetic potential, but challenges come from annual weather variations. This study analyzes how sunflower genotypes respond to different sowing dates under climate change and focuses on the conditions for obtaining maximum seed yields and favorable agronomic traits. From 2020 to 2022, the experiment featured six genotypes sown across four different dates at two-week intervals, simulating seed sunflower production. The results obtained by ANOVA indicated that the seed yield and oil yield were significantly affected by the sowing date, the genotype, and their interaction, with coefficients of variation ranging from 7.6% for oil yield to 41.1% for seed yield. Besides seed yield and oil yield, LDA biplot and Discriminant Functions confirmed that seed germination energy also played a significant role in separating genotypes into clusters. A Visual Mixed Model showed that shifting the optimal sowing date (mid-April) to early May allows a reduction in the number of days the plants spend in critical growth stages, thereby escaping stressful conditions during pollination and seed filling. The findings resulted, on average, in increased yields and improved seed quality, which are the primary goals of seed production, but not in increased 1000-seed weight. Notably, high temperatures during the critical sunflower growth stages negatively affected the measured parameters of seed production. The increased precipitation during seed filling boosted the 1000-seed mass and seed yield. Extended flowering reduced the growth rate and seed germination, but longer seed filling increased the 1000-seed mass and seed yield. Our future breeding goals will be to create genotypes with a shorter flowering period and an extended seed-filling period to better respond to climate change.

Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.

The presence of microplastic particles in agroecosystems has profound implications for soil quality, crop yield, and soil biota. Earthworms are widely recognized as valuable soil bioindicators due to their abundance, fast reproduction, and easy manipulation. The aim of this study was to observe Eisenia fetida avoidance behavior and changes in biomass and mortality rate in soil samples spiked with polyethylene microplastic particles. Three types of soil sampled from the agricultural fields (“Banat 1”, “Banat 2”, and “Bačka”) were tested, as well as three microplastic concentrations (0.1, 0.2, and 0.3%). The calculated avoidance percentages ranged from 18.67% for “Banat 1” and 23.70% for “Banat 2” to 27.40% in the case of “Bačka” soil samples. Generally, E. fetida specimens avoided the sections with plastic in all bioassays: 38.42% of the earthworms were in the chamber section that contained microplastics, as opposed to 61.58% in the control section. The changes in the earthworms’ post-test biomasses were directly proportional to the number of surviving earthworms, with the highest loss in “Bačka” soil samples with 0.3% MPs (−53.05%). The highest mortality rate (46%) was noted in “Bačka” soil samples spiked with the highest concentration of microplastic particles.

Recent European Union regulations aim at reducing the use of chemical pesticides in agriculture. In sunflower production in Serbia, the use of synthetic insecticides for soil and seed treatments has become a prevailing practice for wireworm (Coleoptera: Elateridae) control. However, a number of compounds efficiently used as seed treatments have been phased out. This work aimed at assessing the potential of an environmentally friendly “Attract and Kill” strategy (A&K) for controlling wireworms in sunflower in comparison to conventional insecticides. The experiments were carried out in 2018, 2019 and 2021 involving (a) soil treatments with ATTRACAP^® and a Force 1.5 G; (b) seed treatments with Sonido, Force 20 CS, Lumiposa and Buteo Start 480 FS; (c) controls (i.e., untreated seeds). The efficacy of the treatments was assessed based on the plant density and emerging plant damage (%). A damage rating scale (levels 0–5) was created, aiming for a more reliable and concrete interpretation of the results. Data were processed using binomial and multinomial regressions, followed by modelling of the damage and calculating the odds of damage occurrence, depending on the applied insecticide. In all experimental years and at all localities, the Force 20 CS and/or Force 1.5 G treatments resulted in the highest plant density and the lowest percentage of plant damage. ATTRACAP^® showed good effectiveness, since plant density and percentage of damaged plants were at the same level of significance as the commonly used conventional insecticides in sunflower production (Sonido for seed treatment and Force 1.5 G for soil treatment). Thus, the A&K strategy was efficient in controlling wireworms at conditions of low abundance, based on three-year experimental results. Although the damage rating scale enabled a clear differentiation of plant damage caused by wireworms responsible for reduced plant density, more reliable models were obtained by binomial regressions, classifying plants as damaged or undamaged.